python通过robert、sobel、Laplace算子实现图像边缘提取详解

python通过通过robert、、sobel、、Laplace算子实现图像边缘提取算子实现图像边缘提取

详解详解

主要介绍了python通过robert、sobel、Laplace算子实现图像边缘提取详解,文中通过示例代码介绍的非常详细，

对大家的学习或者工作具有一定的参考学习价值,需要的朋友可以参考下

实现思路：实现思路：

　　1，将传进来的图片矩阵用算子进行卷积求和（卷积和取绝对值）

　　2，用新的矩阵（与原图一样大小）去接收每次的卷积和的值

　　3，卷积图片所有的像素点后，把新的矩阵数据类型转化为uint8

# robert 算子[[-1,-1],[1,1]]

def robert_suanzi(img):

r, c = img.shape

r_sunnzi = [[-1,-1],[1,1]]

for x in range(r):

for y in range(c):

if (y + 2 <= c) and (x + 2 <= r):

imgChild = img[x:x+2, y:y+2]

list_robert = r_sunnzi*imgChild

img[x, y] = abs(list_robert.sum()) # 求和加绝对值

new_imageX = np.zeros(img.shape)

new_imageY = np.zeros(img.shape)

s_suanziX = np.array([[-1,0,1],[-2,0,2],[-1,0,1]]) # X方向

s_suanziY = np.array([[-1,-2,-1],[0,0,0],[1,2,1]])

for i in range(r-2):

for j in range(c-2):

new_imageX[i+1, j+1] = abs(np.sum(img[i:i+3, j:j+3] * s_suanziX))

new_imageY[i+1, j+1] = abs(np.sum(img[i:i+3, j:j+3] * s_suanziY))

new_image[i+1, j+1] = (new_imageX[i+1, j+1]*new_imageX[i+1,j+1] + new_imageY[i+1, j+1]*new_imageY[i+1,j+1])**0.5

# return np.uint8(new_imageX)

r, c = img.shape

new_image = np.zeros((r, c))

L_sunnzi = np.array([[0,-1,0],[-1,4,-1],[0,-1,0]])

# L_sunnzi = np.array([[1,1,1],[1,-8,1],[1,1,1]])

for i in range(r-2):

for j in range(c-2):

new_image[i+1, j+1] = abs(np.sum(img[i:i+3, j:j+3] * L_sunnzi))

return np.uint8(new_image)

# sobel 算子

out_sobel = sobel_suanzi(img)

cv2.imshow('out_sobel_image', out_sobel)